Build your First Neural Network - 15 Minutes, 20 Lines of Code (PyTorch, Google Colab)

In this free 15-minute tutorial, you will build your first neural network from scratch. No previous experience is required. You will be developing your project using Python, PyTorch and Google Colab. You will build and train a neural network to mimic a mystery function.
Follow along the tutorial here:
www.leaky.ai/buildyourfirstne...
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After completing this tutorial, you can attempt the Lemonade sales project (30 minutes, intermediate):
www.leaky.ai/projectlemonade
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#pytorch #python #deeplearning #colab #artificialintelligence #machinelearning

[@dzimitory]

Thank you! It was easy to understand, and I've managed to build my first neural network, following this video!

[@leakyai]

Thank you so much for sharing, that is awesome! If you want, you can now attempt the “Predict Sales” tutorial where you will use a neural network to predict lemonade sales by training it on a synthetic dataset. You can find that video here th-cam.com/video/SugVN2t3M8I/w-d-xo.html or you can follow along the written version here www.leaky.ai/projectlemonade.
If you have any questions, please don’t hesitate to post them below the videos, we are always here to help!
Thanks for watching!

[@alejandroa1443]

Great Video!! Looking forward a video creating a binary classification CNN in Pytorch and Colab!

[@leakyai]

Thank you so much Alejandro for your feedback! Great suggestion, we will put it on our list of future videos to get done! If possible, please help us get the word out for the channel by sharing it with anyone you know that might benefit from the free tutorials! Thanks for watching!

[@Amandaaaaaa123]

thanks so much bruh love you

[@Amandaaaaaa123]

so it trains the model so that it can mimic the mystery function while minimizing loss right

[@leakyai]

That's exactly right! If the loss is going down, it means the neural network is learning how to mimic the mystery function. As a next step, I would encourage you to learn more about training models, especially why there is a need to split our dataset into a separate training, validation and testing set. This is to ensure our model generalizes as it trains and does not overfit or underfit. In this example, we have just trained the model directly on the data. In the realworld, you will want to always ensure your model is trained to generalize the data being used.

[@murilosuzuki1264]

hey man, great video! I tried to train it on the function a*b (I just changed the function at the second code block in "return torch.tensor(a*b)"), but the loss is always very inconsistent and the output value is never even close to right. How should i go about fixing it?

[@leakyai]

Nice work! Could you post your code to GitHub and send me a link? I will then take a look over the weekend and get back to you with a few suggestions.

[@Maestro2050]

and whats next from here?

[@leakyai]

Hey Maestro2050, I would encourage you to learn more about deep learning in general, and then try different projects. Hugging Face is a great place to go as it hosts a massive open source repository of fantastic neural network models that can do amazing things (detect items in an image, convert text from one language to another, create a ChatGPT style chatbot etc.). I would explore that as a next step and build some projects in areas that are you passionate about.